A chart or graph is described in Wikipedia as a type of information graphic or graphic organizer that represents tabular numeric data and/or functions. Charts are often used to make it easier to understand large quantities of data and the relationship between different parts of the data. Charts can usually be read more quickly than the raw data that they come from. They are used in a wide variety of fields, and can be created by hand (often on graph paper) or by computer using a charting application.
Traditional charts use well established and often poorly implemented ways of representing data. Many tools exist to help the user construct very sophisticated representations of data but that sophistication typically results in less meaningful charts. Embodiments of the present invention aim to overcome this problem.
It is known to use charting wizards such as those that are available in Excel and various other systems such as those provided by, for example, IBM. In addition there are multiple Business Intelligence (BI) tools available to users to enable users to analyze data in an attempt to create meaningful feedback. However, as the amount of data increases, so does the complexity of the visual representations created by the analysis of the data. These complex representations can end up swamping parts of the visual representation that is most required and relevant to an end user.
In addition, known systems provide a standardized list of options to all users which the user then must wade through and try and determine which of the options available are most suitable for representing their particular data. This can result in the user mismatching the data being represented with the chosen visual representation so that the resultant representation does not clearly, accurately and succinctly identify any issues with, or convey information about, the data. This can result in the user missing particularly important features of the data due to those features not being represented in the most appropriate manner.
Also, although there are many sophisticated visualization algorithms that do exist and are being developed for specific functions, these algorithms are not provided to a user in a manner that guides the user to easily pick the data to be represented, pick the correct summaries of the data, pick the right dimensions to be represented, pick the right forms of visual representation, or choose unique visual designs to create a collection of visualizations that help someone run their business.
Further, the focus of existing known methods is on providing a single visual design, or type of visual or graphical representation, to represent data. That is, to produce, for example, a single bar graph to be displayed, or a single pie chart to be printed. This is very limiting to a user who may want to show various different aspects of the data in a single document.
Business measures are a well known means of identifying a manageable number of algorithms for which to run a business. However, these business measures merely represent a single dimension of the data, or even only a single number, and so are particularly limiting in respect of the data that they represent. Further, the business measures merely represent data and do not include any further functional capabilities.
This is particularly pertinent to at least any one of the Gaming Industry, Retail Industry, Hospitality Industry, Financial Services Industry, Entertainment Industry and Telecommunications Industry. This is because gaming venues, retail venues, hospitality venues (such as hotels etc.), financial institutions, entertainment distributors and telecommunications companies can collect data, which can be in large volumes, or diverse, detailed, timely or accurate information, on their customers' (e.g. business to customer) purchasing behavior and branch or outlet (business to business) purchasing behavior, as well as movements and activities (e.g. of customers or staff) within the facility in the normal course of providing the relevant business or from external sources.
For example, within the Gaming Industry, data may include the amount gambled by game, how much time has been spent playing each game, what has occurred (e.g., winning of jackpots) during customers' game play. Additionally, similar data is collected regarding non-gaming purchases (e.g., food and beverage, special events, lodging). Finally, customers may be issued credit so data associated with granting credit lines (e.g., credit rating, credit limits, etc.) is also collected.
As a further example, within the Retail Industry, data may include temporal aspects related to an individual transaction such as day, time, day of the week, the proximity of the date of purchase to known holidays. This data may also include special aspects such as the location of the outlet, the relative location of items on the shelves (e.g., aisle, placement within the aisle, height of placement on the aisle). Further examples may include data related to products such as quantity of each individual item purchased, other items in the market basket purchased with the item, price of the items, total value of the transaction, profit margins of the items and an item's shelf life.
As a further example, within the hospitality industry, data may include temporal aspects related to an individual transaction such as day, time, day of the week, the proximity of the date of a hotel/motel visit to known holidays. This data may also include special aspects such as the location of the hotel, the distributions of customers and the demographics of the surrounding area, as well as feeder markets for the facility. Further examples may include data related to products such as the number of room nights a customer spent in the hotel/motel, other items in the market basket purchased such as room service or in-room movies, price of the items, total value of the transaction, profit margins of the items etc.
As a further example, within the financial services industry, data may include temporal aspects related to an individual transaction such as day, time, day of the week, the proximity of the date of purchase to known holidays. This data may also include special aspects such as the location of the branch office like the distributions of customers and the demographics of the surrounding area. Further examples may include data related to products such as quantity of each individual item purchased, other items in the market basket purchased with the item, price of the items, total value of the transaction, profit margins of the items and an item's shelf life.
As a further example, within the entertainment industry, data may include temporal aspects related to an individual transaction such as day, time, day of the week, the proximity of the date of purchase to known holidays. This data may also include special aspects such as the location of the outlet, the relative location of items on the shelves (e.g., aisle, placement within the aisle, height of placement on the aisle). Further examples may include data related to products such as quantity of each individual item purchased, other items in the market basket purchased with the item, price of the items, total value of the transaction, profit margins of the items and an item's shelf life.
As a further example, within the telecommunications industry, data may include temporal aspects related to an individual transaction such as day, time, day of the week, the proximity of the date of purchase to known holidays. This data may also include special aspects such as the location of the telecommunication retail stores, areas of coverage, the distribution of customers and the demographics of the surrounding area. Further examples may include data related to products such as quantity of each individual item purchased, other items in the market basket purchased with the item, price of the items, total value of the transaction, profit margins of the items and an item's shelf life.
These potentially large or dispersed data collections may be further refined by collecting the data so it is available from a centrally accessible point. This centrally accessible capability can be implemented in a number of ways including, a data warehouse or a data mart or a federated information collection.
The often related or diverse and sometimes large volumes of data collected by the Gaming Industry, Retail Industry, Hospitality Industry, Financial Services Industry, Entertainment Industry and Telecommunications Industry on a variety of areas of the business, including data on their customers, outlets or branches (e.g. locations), their operations or external data sets, and in the case of telecommunications, call patterns, can all benefit from methods for understanding this data. These methods may range from the simple analytical views to sophisticated analytical methods as herein described.
R-tree indexing methodologies, as well as other indexing methodologies, are used in conjunction with databases to categorize data and place the data in a hierarchical format. It is known to use self organizing maps to visually represent data. However, self organizing maps can be very difficult and arduous to interpret. Also, it has not previously been known to use the indexing methodologies, in particular the R-tree indexing, as a display mechanism on its own.
Classification algorithms, such as fast clustering genetic algorithms or dimension reduction algorithms, can result in highly complicated structures. These may include 2 displays, the R-Tree, which may provide interactive insight.
For example, in the gaming industry, insight into the relationship between a customers' play, the types of games played, and the location of the game relative to other games.
For example, in the retail industry, insight into the relationship between the value of a customer's purchases, the types of products purchased, and the location of the products purchased relative to other products.
For example, in the hospitality industry, insight into the relationship between the value of a customer's purchases, the types of products and services purchased, and frequency of purchases and each product's use (e.g., food items purchased in a restaurant, in-room movies watched, spa services received) relative to other products.
For example, in the financial industry insight into the relationship between the value of a customer's purchases, the types of products purchased, and frequency of purchases and each product's use (e.g., drawing on a home equity loan, credit card transactions) relative to other products.
For example, in the entertainment industry, insight into the relationship between the value of a customer's purchases, the types of products purchased, and the location of the products purchased relative to other products.
For example, in the telecommunications industry, insight into the relationship between the value of a customer's purchases, the types of products purchased, and frequency of purchases and each product's use (e.g., minutes used, data used, messages sent, premium calls—e.g., foreign calls) relative to other products.
Various other references to the prior art and its associated problems are made throughout the following description.
The present invention aims to overcome, or at least alleviate, some or all of the mentioned problems, or to at least provide the public with a useful choice.